A synthetic energy dataset for non-intrusive load monitoring in households

Abstract

Research on smart grid technologies is expected to result in effective climate change mitigation. Non-Intrusive Load Monitoring (NILM) is seen as a key technique for enabling innovative smart-grid services. By breaking down the energy consumption of households and industrial facilities into its components, NILM techniques provide information on present appliances and can be applied to perform diagnostics. As with related Machine Learning problems, research and development requires a sufficient amount of data to train and validate new approaches. As a viable alternative to collecting datasets in buildings during expensive and time-consuming measurement campaigns, the idea of generating synthetic datasets for NILM gain momentum recently. With SynD, we present a synthetic energy dataset with focus on residential buildings. We release 180 days of synthetic power data on aggregate level (i.e. mains) and individual appliances. SynD is the result of a custom simulation process that relies on power traces of real household appliances. In addition, we present several case studies that demonstrate similarity of our dataset and four real-world energy datasets. Background & Summary Load monitoring is vital for effective and accurate energy monitoring in buildings. Detailed insights can empower further research, help streamlining processes, and improve a building's energy efficiency 1. Introduced in 2 , Non-Intrusive Load Monitoring (NILM) techniques serve to break down a building's aggregate energy consumption to identify active appliances and also to provide diagnostic information. Extensive reviews can be obtained from 3 and 4. NILM can be considered as Machine Learning problem. As such, it requires datasets to train models, to conduct performance evaluation, to evaluate the benefit in real scenarios, and also to perform benchmarking on a common basis. In case of NILM, ground-truth data on aggregate and appliance-level energy consumption are crucial 4. Traditionally, NILM scholarship relies on energy consumption datasets. Such datasets usually contain information on energy consumption on aggregate level (monitored at the mains) and individual loads, which is provided by plug-level meters. Energy consumption datasets are the outcome of measurement campaigns in buildings or industrial facilities, which require expensive measurement equipment, bring bureaucratic burdens, and are time-consuming activities 5. As a viable alternative, the idea of generating synthetic data gain momentum recently. The main motivation behind generating synthetic datasets is to reduce costs for measurement campaigns and save valuable work hours. Instead, custom simulators provide energy consumption datasets on-demand and in contrast to real datasets, without limitations on measurement periods. Furthermore, real datasets suffer from missing readings (gaps), misaligned timestamps, and corrupted data as a result of sensor miscalculation or malfunction 6,7. Synthetic data does not show such issues. With SynD, we present a synthetic energy consumption da...

Review on Deep Neural Networks Applied to Low-Frequency NILM

“…The number of NILM datasets has been increasing over the last years, see [127,128] for recent overviews and [129][130][131] for the most recent published datasets we are aware of. In Table 3, we characterize only the publicly available datasets that have been used in the reviewed studies.…”

Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

“…It is worth noting that extra data generation from the source dataset [20,21] and data simulation [22] are not related to the topic of this research.…”

Review on Deep Neural Networks Applied to Low-Frequency NILM

“…The number of NILM datasets has been increasing over the last years, see [35,36] for recent overviews and [37][38][39] for the most recent published datasets we are aware of. In APPLIANCES: Appliances that have been disaggregated in the corresponding publications are listed in table 3.…”